Abstract
The Machine Learning Pipeline (ML Pipeline) allows the user to fit a model to predict a dependent variable, y, based on a feature set, X. ML Pipeline gives the ability to automatically generate features with its Feature Engineering function, automatically select the most important features with its Feature Selection function, fit the model with its Fit function, and evaluate the model with its Evaluate function. ML Pipeline's functionality can not only help the user predict values for the desired variable it also gives the user a better understanding of which features are important and how effective the model is. ML Pipeline accomplishes all this, while remaining simple to use and interpret.
- Developers:
-
Schiek, Andrew [1] ; Smith, Kandler [1] ; Gasper, Paul [1]
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Release Date:
- 2022-04-28
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
MATLAB
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:AC36-08GO28308CRADAPrimary Award/Contract Number:AC36-08GO28308Other Award/Contract Number:CRD-21-17258
- Code ID:
- 74425
- Site Accession Number:
- NREL SWR-22-35
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Country of Origin:
- United States
Citation Formats
Schiek, Andrew, Smith, Kandler, and Gasper, Paul.
ML Pipeline (Machine Learning Pipeline) [SWR-22-35].
Computer Software.
https://github.com/NREL/Machine-Learning-Pipeline.
USDOE, CRADA.
28 Apr. 2022.
Web.
doi:10.11578/dc.20221006.5.
Schiek, Andrew, Smith, Kandler, & Gasper, Paul.
(2022, April 28).
ML Pipeline (Machine Learning Pipeline) [SWR-22-35].
[Computer software].
https://github.com/NREL/Machine-Learning-Pipeline.
https://doi.org/10.11578/dc.20221006.5.
Schiek, Andrew, Smith, Kandler, and Gasper, Paul.
"ML Pipeline (Machine Learning Pipeline) [SWR-22-35]." Computer software.
April 28, 2022.
https://github.com/NREL/Machine-Learning-Pipeline.
https://doi.org/10.11578/dc.20221006.5.
@misc{
doecode_74425,
title = {ML Pipeline (Machine Learning Pipeline) [SWR-22-35]},
author = {Schiek, Andrew and Smith, Kandler and Gasper, Paul},
abstractNote = {The Machine Learning Pipeline (ML Pipeline) allows the user to fit a model to predict a dependent variable, y, based on a feature set, X. ML Pipeline gives the ability to automatically generate features with its Feature Engineering function, automatically select the most important features with its Feature Selection function, fit the model with its Fit function, and evaluate the model with its Evaluate function. ML Pipeline's functionality can not only help the user predict values for the desired variable it also gives the user a better understanding of which features are important and how effective the model is. ML Pipeline accomplishes all this, while remaining simple to use and interpret.},
doi = {10.11578/dc.20221006.5},
url = {https://doi.org/10.11578/dc.20221006.5},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20221006.5}},
year = {2022},
month = {apr}
}